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Cited by 483

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy

Open access
Wang, Pu, Xiao, Xiao, Glissen Brown, Jeremy R., Berzin, Tyler M., Tu, Mengtian, Xiong, Fei, Hu, Xiao, Liu, Peixi, Song, Yan, Zhang, Di, Yang, Xue, Li, Liangping, He, Jiong, Yi, Xin, Liu, Jingjia, Liu, Xiaogang·2018·Nature Biomedical Engineering
DOI 10.1038/s41551-018-0301-3
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Abstract

The detection and removal of precancerous polyps via colonoscopy is the gold standard for the prevention of colon cancer. However, the detection rate of adenomatous polyps can vary significantly among endoscopists. Here, we show that a machine-learning algorithm can detect polyps in clinical colonoscopies, in real time and with high sensitivity and specificity. We developed the deep-learning algorithm by using data from 1,290 patients, and validated it on newly collected 27,113 colonoscopy images from 1,138 patients with at least one detected polyp (per-image-sensitivity, 94.38%; per-image-specificity, 95.92%; area under the receiver operating characteristic curve, 0.984), on a public database of 612 polyp-containing images (per-image-sensitivity, 88.24%), on 138 colonoscopy videos with histologically confirmed polyps (per-image-sensitivity of 91.64%; per-polyp-sensitivity, 100%), and on 54 unaltered full-range colonoscopy videos without polyps (per-image-specificity, 95.40%). By using a multi-threaded processing system, the algorithm can process at least 25 frames per second with a latency of 76.80 ± 5.60 ms in real-time video analysis. The software may aid endoscopists while performing colonoscopies, and help assess differences in polyp and adenoma detection performance among endoscopists. A deep-learning algorithm can detect polyps in the colon in real time and with high sensitivity and specificity, according to validation studies with prospectively collected images and videos from colonoscopies performed in 1,138 patients.

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Metadata

Authors
Wang, P., Xiao, X., Glissen Brown, J. R., Berzin, T. M., Tu, M., Xiong, F., Hu, X., Liu, P., Song, Y., Zhang, D., Yang, X., Li, L., He, J., Yi, X., Liu, J., & Liu, X.
Venue
Nature Biomedical Engineering
Publisher
Springer Science and Business Media LLC
Published
2018-10-10
Volume
2
Issue
10
Pages
741-748
ISSN
2157-846X (ISSN-L)
DOI
10.1038/s41551-018-0301-3
OpenAlex
W2894010682
Cited by
483
References
6
FWCI
18.79
Type
journal article
Language
en
Open access
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